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Systems engineering practices are evolving to address fast-changing needs in fielding complex systems. These needs create an environment in which system needs evolve or change too quickly to be tracked or managed by humans' natural capabilities. We propose that systems engineering must aid systems engineering managers by providing architectural alternatives and design options. Further, as systems become more complex and dynamic, there is an increased need to identify hidden risks, model emergent behavior, and expose hidden patterns in the behavior of stakeholders. Systems engineering needs to evolve to build fast-fielded, resilient, and adaptive systems that leverage positive reinforcement feedback loops with multiple experimental and real-world information sources. The very basis of systems engineering must evolve from today's development paradigms to a future that leverages modeling, simulation, and artificial intelligence to drastically improve the capability and agility for developing new systems. This paper proposes a common way forward to enable this new form of complex adaptive systems engineering. [ABSTRACT FROM AUTHOR]

Understanding is the essence of any intelligent system. We revise our four machine understanding paradigms which are: (i) basic understanding, (ii) rich understanding, (iii) exploratory understanding, and (iv) theory-based understanding; and we elaborate on the first two of them. We then introduce the concept of two-stage (or deep) machine understanding which provides descriptive understandings, as well as evaluations of these understandings. After a brief systematization of emotions, we cover the following paradigms for agents with two-stage (deep) understanding abilities for emotional intelligence simulation: (i) basic understanding, (ii) rich-understanding, and (iii) switchable understanding. [ABSTRACT FROM AUTHOR]